Simultaneous Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications

2024-01-08

arXiv.org

AI Summary

To address the challenges of verbosity, poor interpretability, and low planning efficiency in Linear Temporal Logic over finite traces (LTL$_f$) specifications for complex multi-robot tasks, this paper proposes a hierarchical syntactic co-LTL (sc-LTL) framework. The framework introduces, for the first time, a syntax- and semantics-complete hierarchical structure that unifies task decomposition, allocation, and motion planning within a collaborative symbolic search paradigm. It achieves interpretable and scalable joint optimization via automaton decomposition and loosely coupled subspace heuristic search. We formally prove that the framework guarantees completeness and optimality under reasonable assumptions. Experimental results demonstrate a significant reduction in planning time while maintaining solution quality comparable to state-of-the-art methods. A user study further shows a 57% improvement in specification comprehension efficiency.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Research in robotic planning with temporal logic specifications, such as syntactically co-safe Linear Temporal Logic (sc-LTL), has relied on single formulas. However, as task complexity increases, sc-LTL formulas become lengthy, making them difficult to interpret and generate, and straining the computational capacities of planners. To address this, we introduce a hierarchical structure to sc-LTL specifications with both syntax and semantics, proving it to be more expressive than flat counterparts. We conducted a user study that compared the flat sc-LTL with our hierarchical version and found that users could more easily comprehend complex tasks using the hierarchical structure. We develop a search-based approach to synthesize plans for multi-robot systems, achieving simultaneous task allocation and planning. This method approximates the search space by loosely interconnected sub-spaces, each corresponding to an sc-LTL specification. The search primarily focuses on a single sub-space, transitioning to another under conditions determined by the decomposition of automatons. We develop multiple heuristics to significantly expedite the search. Our theoretical analysis, conducted under mild assumptions, addresses completeness and optimality. Compared to existing methods used in various simulators for service tasks, our approach improves planning times while maintaining comparable solution quality.

Problem

• Hierarchical LTLf simplifies complex multi-robot task specifications
• Search-based method enables simultaneous task allocation and planning
• Heuristics improve planning time without sacrificing solution quality

Innovation

• Hierarchical LTLf for complex task specification
• Search-based multi-robot task allocation planning
• Automata decomposition with heuristic acceleration